This report summarizes work done in the summer and fall of 2020 to prototype the integration of advanced machine learning techniques and Opencog symbolic inference for applications in applied biomedical research. Run the notebook from the cancer repo reports directory to reproduce this R notebook output.
describe cbd package
describe coincide study subset
describe normalization for meta-analysis
Spectral bigraphs1 spectal graph ref are a blah blah blah…
In the raw combined data we can easily see the study source bias. In particular, [GSE9893 & GSE20194 blah blah]. Note the two channel studies marked with crosses in the lower right and top center of the principle component plots (name 3 studies? where is third in first plot?)
describe infogan & include image from google doc
This study blah blah blah
The outlier dataset examining relapse outcome in adjuvant tamoxifan treatment (PMID: 183471752 Chanrion M, Negre V, et al. A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer. Clin Cancer Res. 2008 Mar 15;14(6):1744-52. doi: 10.1158/1078-0432.CCR-07-1833; PMCID: PMC2912334.)
RFS ~ 1 + p5 + age + node + radio + tumor + grade + pr vs RFS ~ 1 + pam + age + node + radio + tumor + grade + pr